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About This Role
Job Description: AI Consulting Engineering Lead
About EXL:
EXL, from our beginnings in business process services to becoming a global leader in data and AI, EXL brings 25\+ years of proven expertise helping enterprises transform and redefining what's possible for our clients.
We partner with leading companies in insurance, healthcare, banking and capital markets, retail, media and communications, and energy and infrastructure to reimagine business models, deliver measurable outcomes, and accelerate innovation. While nearly 70% of enterprise AI initiatives fail, EXL consistently delivers a 90% success rate by integrating deep industry knowledge, robust data and analytics capabilities, and cutting\-edge AI implementation in client workflows—turning complexity into value at scale.
At EXL, innovation isn't just a buzzword—it's how we solve complex challenges and create lasting value. By integrating cutting\-edge technologies such as Agentic AI into workflows and data, and cloud computing, we empower our clients to remain at the forefront of innovation. Whether it's revolutionizing customer experiences, streamlining operations, or uncovering new revenue streams, innovation fuels everything we do.
About the Job:
Drive the architecture, design, and engineering solutioning of scalable AI\-powered enterprise platforms across EXL’s strategic engagements. This leader will anchor the technology vision across Data, CX, AI, and Automation layers, supporting both sales pursuits and client delivery. This leader will also be responsible for leading Deep Tech capability, partner ecosystem and client pursuits.
Responsibilities: Key Responsibilities:
- Lead Deep tech partner ecosystem and practice
- Collaborate with EXL Labs and partner ecosystems (Google, Microsoft, Salesforce, UiPath, ServiceNow, AWS, Appian etc.) to integrate emerging capabilities into client solutions.
- Set up Deep tech labs with Nvidia, Open AI, Anthropic, Groq etc.
- Identify and incubate emerging technology use cases (e.g., Agentic, Quantum) relevant to client industries.
- Client Pursuit \& Technical Solutioning
- Lead the technical pre\-sales process, engaging directly with clients to understand business goals, pain points, and transformation needs.
- Develop end\-to\-end solution architectures across data platforms, cloud ecosystems (AWS, Azure, GCP), AI/ML, IoT, and other deep tech domains.
- Author and present compelling technical proposals, RFP/RFI responses, and solution blueprints that resonate with client stakeholders.
- Engage in RFP/RFI cycles and deal reviews to ensure technical integrity and differentiation.
- Transformation Consulting \& Advisory
- Act as a strategic advisor to clients on digital transformation, data modernization, cloud migration, and emerging AI adoption.
- Assess current state architecture and co\-create AI led transformation roadmaps aligned with future\-state vision and business priorities.
- Lead client and internal architecture workshops
- Shape AI\-first enterprise designs integrating data platforms, automation, and intelligent workflows.
- Partner with cross\-functional teams across domain, tech, data, and AI to define solutions for large transformation deals.
- Translate business objectives into executable technology roadmaps with clear ROI and scalability.
- Define architectural guardrails, reusable components, and accelerators for repeatable deployment patterns.
- Mentor solution and engineering teams to adopt modern design principles
Qualifications: Qualifications:
- 15\+ years in enterprise architecture, engineering, or solution leadership within consulting, SI, or digital practices.
- Proven experience in architecting data\-driven, AI\-enabled enterprise platforms.
- Understanding of cloud\-native architectures, integration frameworks, data fabric/mesh, and AI orchestration.
- Stakeholder management and executive communication skills.
The typical base pay range for this role across the U.S. is USD $201,500 \- $257,000 per year.
*The posted range is the hiring range for this role — a subset of the broader range available to employees over time — and reflects base salary across our national hiring scale. Final offers are based on several factors, including the candidate's skills and experience, internal pay equity, work location, market conditions for the role, and the specific scope and responsibilities of the position. The top of the range is reserved for candidates who notably exceed the requirements; the lower end applies to those with less experience or fewer preferred qualifications. For positions based in higher\-cost zones (e.g., California, New York, New Jersey), actual compensation may exceed the posted range; your recruiter will share specifics during the process.*
For more information on benefits and what we offer please visit us at https://www.exlservice.com/us\-careers\-and\-benefits
Salary Context
This $201K-$257K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 1841 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At EXL Service, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $218,750 based on 3,817 positions with disclosed compensation. This role's midpoint ($229K) sits 5% above the category median. Disclosed range: $201K to $257K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
EXL Service AI Hiring
EXL Service has 6 open AI roles right now. They're hiring across AI Architect, AI/ML Engineer. Positions span US, Jersey City, NJ, US. Compensation range: $170K - $257K.
Location Context
AI roles in Austin pay a median of $214,343 across 87 tracked positions.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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